Enhancing Surgical Precision and Recovery: A Review of Medical Robotics and AI Integration in Modern Operating Rooms

Abstract

This study presents a thorough technique for creating and assessing intelligent medical robotic systems, based on the integration of artificial intelligence (AI), sophisticated sensor networks, and autonomous control frameworks. The proposed approach is multi-phase optimization from preoperative planning to intraoperative assistance and postoperative monitoring. Data acquisition from various biomedical sensors, with AIdriven diagnostic inference, is key, but adaptive learning via federated and explainable AI (XAI) models is the key module. Furthermore, the methodology brings into play recent imaging, haptics and integrates with the Internet of Medical Things (IoMT) to promote surgical precision and reduce human error. The performance benchmarking under clinical constraints is proposed to be executed via simulation-based testing and real-time validation. We also discuss applications in case-driven evaluations of robotics applications in robotic-assisted orthopaedic and microsurgical procedures to demonstrate usefulness. The intention of this study is to bridge the technological gap between the current robotic systems to future autonomous healthcare products. This methodology can be used as a blueprint for the researchers and developers working at the robotics and AI healthcare intersection to provide for the innovation, safety, and operational efficiency in surgical environments.